When video or image stream is manipulated, for example when a video is compressed so that it may be stored on a DVD or transmitted over a television channel, or when video is transcoded from one format to another, the manipulations introduce artifacts from the compression or conversion into the resultant video. Sometimes these artifacts are invisible or imperceptible to human viewers, due to physiology and phenomena of the human eye and the way it perceives light and motion. Other times the artifacts are plainly visible. It is nearly impossible to determine how manipulated video will be perceived by simply analyzing the resultant video data.
Instead, video quality is best determined by a survey of a statistically significant number of people gathered to grade the quality of the video. Audiences watch the video and grade it on a predetermined quality scale. Such audience surveys, however, are impractical and cost prohibitive for most applications, and thus methods of automating picture quality rating were developed.
Full reference (FR) picture quality analyzers are machines that compare two images, or two image streams, and generate a “score” or other measure of how humans would perceive the quality of the video conversion, or the match between the original and the modified test streams. A high score indicates a high-quality conversion, whereas a low score indicates that the resultant video is a poor representation of its original.
A disconnect exists, though, in that the state of the art picture quality analyzers either 1) do not include color analysis in their calculations on video quality or 2) include simple objective measures such as color PSNR (peak signal to noise ratio) or color impairment measurements such as blocking or ringing, but do not take into account the human vision system's adaptation mechanisms causing drastic changes in sensitivity to color changes depending on spatiotemporal contexts.
An example picture quality measurement device that does not include color analysis in their calculations on video quality is the Tektronix PQA500. Analysis is made on luminance (light intensity) values only. Although there have been published proposals of adding filters to color appearance models, they so far have been fixed models (such as static Gaussian spatial filters). In the luminance perception model of the PQA500, adaptive response is relative to an integrated adaptation point represented as the output of a “surround” spatiotemporal filter with upper resolution in time and space set by a “center” spatiotemporal filter. This luminance only method, being state of the art for detecting perceptible errors in intensity, has been quite sufficient for most video transmission system assessment applications, mostly due to the tendency for luminance error and chroma error to correlate well. However, increasingly complex systems and flexibility in encoders and other video processing devices has lead to increased chances of impairments primarily seen as color errors. For example, in video broadcast and webcast, oftentimes colorimetry of input video and one or more processing or display components is mismatched; out of gamut colors may be clipped, and in some cases out of gamut colors may overflow into an opposite color. Luminance analysis fails to detect the majority of these errors, and consequently the quality rating becomes inaccurate.
Embodiments of the invention address these and other limitations in the prior art.